Document classification using a Bi-LSTM to unclog Brazil's supreme court
This addresses a domain-specific issue for Brazil's judiciary system by automating document classification to reduce backlog, but it appears incremental as it applies an existing method to new data.
The paper tackles the problem of classifying legal documents in Brazil's clogged supreme court system using a Bi-LSTM network to associate tags and allocate cases to teams, but no concrete results or numbers are provided.
The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to relevant tags and allocated to the right team. Most of the cases reach the court as raster scanned documents with widely variable levels of quality. One of the first steps for the analysis is to classify these documents. In this paper we present a Bidirectional Long Short-Term Memory network (Bi-LSTM) to classify these pieces of legal document.